Trackline navigation data collected in Nantucket Sound Massachusetts in the vicinity of Horseshoe Shoal, during USGS Field Activity 2022-001-FA using a Teledyne SeaBat Integrated Dual-Head (IDH) T20-P multibeam echosounder (Esri polyline shapefile, Geographic, WGS 84)

PROCESSING STEP 1: QIMERA DATA PROCESSING.
Multibeam bathymetry processing within Qimera multibeam processing software (version 2.5.0) during the survey consisted of the following workflow:
1) A new Qimera project was created with projection information set to Universal Transverse Mercator (UTM) Zone 19N, WGS 84. The s7k files for each Julian day were imported to the project using the Source/add raw sonar files menu.
2) A Vessel configuration file was created with the linear and angular installation offsets for each T20-P sonar head as well as vendor specified uncertainty values for each of the survey sensors.
3) Sound Velocity profiles for each day were imported and converted to Qimera using the Source/import/Caris svp menu. Each profile was reviewed for incorrect records using the SVP editor. Incorrect records near the surface or within the water column were flagged using the "reject selection" context menu.
4) Predicted Tides from the Woods Hole, MA tidal station (id 8447930) for the month of June, were downloaded and imported using the Source/Add Tide Files menu. Predicted tides were referenced to Mean Lower Low Water (MLLW) at 6-minute intervals.
5) Delayed heave data from the raw POS MV files (.000) were used to update raw sonar lines using the Source/add binary navigation menu function.
6) The Processing Settings Editor was used to establish the following:
a) Sound velocity Strategy: nearest in time, Use first entry as raytrace profile.
b) Position, Motion, and Heading Source Priorities: were set position 1, Motion 1, and Motion 1 respectively.
c)Vertical Referencing was set to the Woods Hole predicted Tide file.
d) Blocking filters were set to Across Track = 0-to-80 meters on each sonar head. Colinearity Fail was also selected.
7)Each raw sonar file was processed using the settings described above.
8) A preliminary, 2-m resolution dynamic surface (2022-001-FA_2m_Draft) was created and reviewed for inconsistencies and anomalies. The swath editor was used to remove spurious points through manual editing and filter application.
The contact person for this and all subsequent processing steps below is Brian Andrews.

PROCESSING STEP 2: APPLY POST PROCESSED SBET FILES AND EDIT SOUNDINGS.
Post-survey processing within Qimera (version 2.5.0) consisted of the following workflow:
1) Post-processed navigation, vessel attitude, and GNSS height data from POSPac Smoothed Best Estimate of Trajectory (SBET) files, and post-processed RMS attitude error data from POSPac smrmsg files were used to update each Sonar file using the Source/Add Binary Navigation Files function. The SBET files were referenced using the WGS 84 Ellipsoid (meters).
2) Once the sbet and smrmsg files were imported for each raw sonar file, the Processing Settings Editor was used to replace the attitude and tide referencing data using the following settings:
a) Position, Motion, and Heading Source Priorities were superseded by "sbet" file.
b) Vertical Referencing Method was set to "RTK (Accurate Height)" using the "sbet" file.

PROCESSSING STEP 3: EXPORT TRACKLINE NAVIGTATION AND CONVERT TO SHAPEFILE.
Processed navigation for each sonar file was exported from Qimera using the Export/Raw Sonar File/Export Trackline to ASCII menu. A python Jupyter notebook utilized the python modules pandas (version 1.5.2), geopandas (version 0.12.1), pyproj (version 3.4.0), shapely (version 1.8.5.post1), and sqlite3 (version 3.40.0) to process the navigation data for each profile line in the following steps. The separate ascii files were imported to pandas dataframes and sub-sampled to preserve the first and last records and records at ten second intervals in between. Coordinates for the reduced data were transformed from UTM Zone 19N (meters) to WGS84 geographic (decimal degrees), UTC date time objects reformatted to produce columns containing the filename, a combined year-julian day number string, a combined Julian day number UTC time string, survey id, vehicle id, and device id. Geopandas and shapely were used to create a point geometry column from the geographic coordinates. SQL queries was used to load each navigation dataframe into a geospatial SQLite database using the pyspatialite interface. Records were appended to two database tables, the first containing the reduced coordinate navigation, and the second containing trackline features created from the coordinated navigation point geometries. The trackline table was imported into QGIS (v.3.26) from the SQLite database, then exported (Right click on database feature class > Data > Save Features As) to the new Esri point and polyline shapefile ‘2022-001-FA_TeledyneT20P_Tracklines’.. Additional processing included adding and calculating the fields described in the Entity and Attribute Section below.